pair_coordinates¶

pair_coordinates(set_a, set_b, radius)[source]¶

Find optimal unique pairing between two sets of coordinates.

This function uses the Munkres or Kuhn-Munkres algorithm behind the scene to find the most optimal unique pairing when pairing points in set B against points in set A, using Euclidean distance as the cost function.

Parameters:

  • set_a (np.ndarray) – An array of shape Nx2 contains the of XY coordinate of N different points.

  • set_b (np.ndarray) – An array of shape Mx2 contains the of XY coordinate of M different points.

  • radius (float) – Valid area around a point in set A to consider a given coordinate in set B a candidate for matching.

Returns:

  • numpy.ndarray - Pairing:

    An array of shape Kx2, each item in K contains indices where point at index [0] in set A paired with point in set B at index [1].

  • numpy.ndarray - Unpaired A:

    Indices of unpaired points in set A.

  • numpy.ndarray - Unpaired B:

    Indices of unpaired points in set B.

Return type:

tuple

Examples

>>> from tiatoolbox.utils.metrics import pair_coordinates
>>> # Generate two random example sets; replace with your own data
>>> import numpy as np
>>> np.random.seed(6)
>>> set_a_num_points = np.random.randint(low=10, high=30)
>>> set_b_num_points = np.random.randint(low=10, high=30)
>>> set_a = np.random.randint(low=0, high=25, size=(set_a_num_points, 2))
>>> set_b = np.random.randint(low=0, high=25, size=(set_b_num_points, 2))
>>> radius = 2.0
>>> # Example usage of pair_coordinates
>>> pairing, unpaired_a, unpaired_b = pair_coordinates(set_a, set_b, radius)